Astronomical Site Selection for Turkey Using GIS Techniques

A site selection of potential observatory locations in Turkey have been carried out by using Multi-Criteria Decision Analysis (MCDA) coupled with Geographical Information Systems (GIS) and satellite imagery which in turn reduced cost and time and increased the accuracy of the final outcome. The layers of cloud cover, digital elevation model, artificial lights, precipitable water vapor, aerosol optical thickness and wind speed were studied in the GIS system. In conclusion of MCDA, the most suitable regions were found to be located in a strip crossing from southwest to northeast including also a diverted region in southeast of Turkey. These regions are thus our prime candidate locations for future on-site testing. In addition to this major outcome, this study has also been applied to locations of major observatories sites. Since no goal is set for \textit{the best}, the results of this study is limited with a list of positions. Therefore, the list has to be further confirmed with on-site tests. A national funding has been awarded to produce a prototype of an on-site test unit (to measure both astronomical and meteorological parameters) which might be used in this list of locations.Comment: 17 pages, 10 figures, accepted by Experimental Astronom

SPATIALLY EXPLICIT MODEL OF AREAS BETWEEN SUITABLE BLACK BEAR HABITAT IN EAST TEXAS AND BLACK BEAR POPULATIONS IN LOUISIANA, ARKANSAS, AND OKLAHOMA

Although black bears (Ursus americanus, Ursus americanus luteolus) were once found throughout the south-central United States, unregulated harvest and habitat loss resulted in severe range retractions and by the beginning of the twentieth century populations in Oklahoma, Louisiana, Texas and Arkansas were nearing extirpation. In response to these losses, translocation programs were initiated in Arkansas (1958-1968 & 2000-2006) and Louisiana (1964-1967 & 2001-2009). These programs successfully restored bears to portions of Louisiana and Arkansas, and, as populations in Arkansas began dispersing, to Oklahoma. In contrast, east Texas remains unoccupied despite the existence of suitable habitat in the region. To facilitate the establishment of a breeding population in east Texas, I sought to identify suitable habitat which bears could use for dispersal between known bear locations in Louisiana, Arkansas and Oklahoma and the east Texas recovery units. I utilized Maxent, a machine learning software, to model habitat suitability in this region. I collected known black bear presence locations (n=18,241) from state agencies in Louisiana, Oklahoma, Arkansas and east Texas and filtered them to reduce spatial autocorrelation (n=664). I also collected spatial data sets based on known black bear ecology to serve as environmental predictor variables. The model was developed at 30-m resolution and encompassed 417,076 km 2. The final model was selected to minimize model over-fitting while maintaining a high test Area Under the Receiver Operating Curve (AUC TEST)score. For final model interpretation and analysis, I used the 10th percentile training threshold available in Maxent which excludes the lowest 10% of predicted presence suitability scores from the binary predictive map, thus resulting in a more conservative predictive map. The final 10th percentile model predicted 43.7% of the pixels in the study area as suitable and 53.7 % percent of the pixels identified as potential recovery units by Kaminski et al. (2013, 2014) as suitable. To focus management efforts, I identified three movement zones with a high proportion of suitable habitat within which connectivity analyses were performed. Suitable patches greater than or equal to 12 km2 were classified within ArcGIS as stepping stone patches. Buffers of 3,500 m were generated around these patches to determine the level of functional connectivity in each zone. The final Maxent model confirmed that suitable bear habitat exists between source populations and the east Texas recovery units. The importance of percent of mast producing forest, percentage of cultivated crops and percentage of protected lands reflect what is known about basic bear biology and ecology. Furthermore, 153 stepping stone patches were identified within the movement zones, demonstrating that there is a reasonable chance of bears naturally dispersing to east Texas using the habitat identified in this study. Thus, protection of existing bear habitat and the stepping stone patches identified in this study should be a priority for managers seeking to facilitate natural bear recolonization of east Texas

Active Sampling-based Binary Verification of Dynamical Systems

Nonlinear, adaptive, or otherwise complex control techniques are increasingly relied upon to ensure the safety of systems operating in uncertain environments. However, the nonlinearity of the resulting closed-loop system complicates verification that the system does in fact satisfy those requirements at all possible operating conditions. While analytical proof-based techniques and finite abstractions can be used to provably verify the closed-loop system's response at different operating conditions, they often produce conservative approximations due to restrictive assumptions and are difficult to construct in many applications. In contrast, popular statistical verification techniques relax the restrictions and instead rely upon simulations to construct statistical or probabilistic guarantees. This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets. Binary evaluations of closed-loop system requirement satisfaction at various realizations of the uncertainties are obtained through temporal logic robustness metrics, which are then used to construct predictive models of requirement satisfaction over the full set of possible uncertainties. As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction error. Various case studies demonstrate the closed-loop verification procedure and highlight improvements in prediction error over both existing analytical and statistical verification techniques.Comment: 23 page

Assessing the suitable habitat for reintroduction of brown trout (Salmo trutta forma fario) in a lowland river : a modeling approach

Huge efforts have been made during the past decades to improve the water quality and to restore the physical habitat of rivers and streams in western Europe. This has led to an improvement in biological water quality and an increase in fish stocks in many countries. However, several rheophilic fish species such as brown trout are still categorized as vulnerable in lowland streams in Flanders (Belgium). In order to support cost-efficient restoration programs, habitat suitability modeling can be used. In this study, we developed an ensemble of habitat suitability models using metaheuristic algorithms to explore the importance of a large number of environmental variables, including chemical, physical, and hydromorphological characteristics to determine the suitable habitat for reintroduction of brown trout in the Zwalm River basin (Flanders, Belgium), which is included in the Habitats Directive. Mean stream velocity, water temperature, hiding opportunities, and presence of pools or riffles were identified as the most important variables determining the habitat suitability. Brown trout mainly preferred streams with a relatively high mean reach stream velocity (0.2-1m/s), a low water temperature (7-15 degrees C), and the presence of pools. The ensemble of models indicated that most of the tributaries and headwaters were suitable for the species. Synthesis and applications. Our results indicate that this modeling approach can be used to support river management, not only for brown trout but also for other species in similar geographical regions. Specifically for the Zwalm River basin, future restoration of the physical habitat, removal of the remaining migration barriers and the development of suitable spawning grounds could promote the successful restoration of brown trout

Development of titanium dioxide nanoparticles/nanosolution for photocatalytic activity

Biological and chemical contaminants by man-made activities have been serious global issue. Exposure of these contaminants beyond the limits may result in serious environmental and health problem. Therefore, it is important to develop an effective solution that can be easily utilized by mankind. One of the effective ways to overcome this problem is by using titanium dioxide (TiO2). TiO2 is a well-known photocatalyst that widely used for environmental clean-up due to its ability to decompose organic pollutant and kill bacteria. Although it is proven TiO2 has an advantage to solve this concern, its usefulness unfortunately is limited only under UV light irradiation. Therefore, the aim of this work was to investigate the potential of TiO2 that can be activated under visible light by the incorporation of metal ions (Fe, Ag, Zr and Ag-Zr). In this study, sol-gel method was employed for the synthesis of metal ions incorporated TiO2. XRD analysis revealed that all samples content biphasic anatase-brookite TiO2 of size 3 nm to 5 nm. It was found that the incorporation of these metal ions did not change the morphology of TiO2 but the crystallinity and optical properties were affected. The crystallinity of anatase in the biphasic TiO2 was found to be decreased and favored brookite formation. PL analysis showed metal ions incorporation suppressed the recombination of electron-hole pairs while the band gap energy of TiO2 (3.2 eV) was decreased by the incorporation of Fe (2.46 eV) and Ag (2.86 eV). Among this incorporation, Ag-Zr incorporated TiO2 showed highest performance for methyl orange degradation (93%) under fluorescent xxv light irradiation for 10 h. This follows by Zr-TiO2 (82%), Fe-TiO2 (75%) and Ag�TiO2 (43%). Meanwhile, the highest antibacterial performance was exhibited by Ag�TiO2. TEM images showed that E.coli bacterium was killed within 12 h after treated with Ag-TiO2. The results obtained from the fieldwork study established that Ag-Zr incorporation have excellent performances for VOC removal and antibacterial test. The VOC content after treated with Ag-Zr-TiO2 fulfilled the Industry Code of Practice on Indoor Air Quality 2010 which is lower than 3 ppm. In addition, the percentage of microbes also found to be decrease around 45 % within 5 days of monitoring

Collection and integration of local knowledge and experience through a collective spatial analysis

This article discusses the convenience of adopting an approach of Collective Spatial Analysis in the P/PGIS processes, with the aim of improving the collection and integration of knowledge and local expertise in decision-making, mainly in the fields of planning and adopting territorial policies. Based on empirical evidence, as a result of the review of scientific articles from the Web of Science database, in which it is displayed how the knowledge and experience of people involved in decision-making supported by P/PGIS are collected and used, a prototype of a WEB-GSDSS application has been developed. This prototype allows a group of people to participate anonymously, in an asynchronous and distributed way, in a decision-making process to locate goods, services, or events through the convergence of their views. Via this application, two case studies for planning services in districts of Ecuador and Italy were carried out. Early results suggest that in P/PGIS local and external actors contribute their knowledge and experience to generate information that afterwards is integrated and analysed in the decision-making process. On the other hand, in a Collective Spatial Analysis, these actors analyse and generate information in conjunction with their knowledge and experience during the process of decision-making. We conclude that, although the Collective Spatial Analysis approach presented is in a subjective and initial stage, it does drive improvements in the collection and integration of knowledge and local experience, foremost among them is an interdisciplinary geo-consensusPeer ReviewedPostprint (published version

A site selection model to identify optimal locations for microalgae biofuel production facilities in sicily (Italy)

The lack of sustainability and negative environmental impacts of using fossil fuel resources for energy production and their consequent increase in prices during last decades have led to an increasing interest in the development of renewable biofuels. Among possible biomass fuel sources, microalgae represent one of the most promising solutions. The present work is based on the implementation of a model that facilitates identification of optimal geographic locations for large-scale open ponds for microalgae cultivation for biofuels production. The combination of a biomass production model with specific site location parameters such as irradiance, geographical constraints, land use, topography, temperatures and CO2 for biofuels plants were identified in Sicily (Italy). A simulation of CO2 saved by using the theoretical biofuel produced in place of traditional fuel was implemented. Results indicate that the territory of Sicily offers a good prospective for these technologies and the results identify ideal locations for locating biomass fuel production facilities. Moreover, the research provides a robust method that can be tailored to the specific requirements and data availability of other territories. © Research India Publications

Domain-Type-Guided Refinement Selection Based on Sliced Path Prefixes

Abstraction is a successful technique in software verification, and interpolation on infeasible error paths is a successful approach to automatically detect the right level of abstraction in counterexample-guided abstraction refinement. Because the interpolants have a significant influence on the quality of the abstraction, and thus, the effectiveness of the verification, an algorithm for deriving the best possible interpolants is desirable. We present an analysis-independent technique that makes it possible to extract several alternative sequences of interpolants from one given infeasible error path, if there are several reasons for infeasibility in the error path. We take as input the given infeasible error path and apply a slicing technique to obtain a set of error paths that are more abstract than the original error path but still infeasible, each for a different reason. The (more abstract) constraints of the new paths can be passed to a standard interpolation engine, in order to obtain a set of interpolant sequences, one for each new path. The analysis can then choose from this set of interpolant sequences and select the most appropriate, instead of being bound to the single interpolant sequence that the interpolation engine would normally return. For example, we can select based on domain types of variables in the interpolants, prefer to avoid loop counters, or compare with templates for potential loop invariants, and thus control what kind of information occurs in the abstraction of the program. We implemented the new algorithm in the open-source verification framework CPAchecker and show that our proof-technique-independent approach yields a significant improvement of the effectiveness and efficiency of the verification process.Comment: 10 pages, 5 figures, 1 table, 4 algorithm

Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing

Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS, to efficiently build fine-grained AQI maps in realtime. Specifically, we first propose the Gaussian plume model on basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a two-dimensional open space like a roadside park, and a three-dimensional space like a courtyard inside a building. Experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm